Public Health Improvements as a Result of Data Usage and Analysis in Healthcare

Big data has made a slow transition from being a vague boogie man to being a force of profound and meaningful change. Though it’s far from reaching its full potential, data is already having an enormous impact on
healthcare outcomes across the world — both at the public and individual levels.

In this article, we take a look at how public health improvements can stem from data analysis in the healthcare industry.

During the height of the pandemic, it became extremely important to use every resource to its fullest effect. Hospitals were dealing with scarcity at a level they’d never seen before. There weren’t enough beds. There weren’t enough healthcare professionals. The people they did have got sick regularly, and were then sidelined for two weeks.

For a few months, there weren’t even enough basic things, like masks, or hand sanitizer to go around. During this period of enormous need, data implementation was able to bridge service gaps. It couldn’t make a ventilator appear out of thin air, but it could help administrators and staff
members know how to use the resources they did have effectively.

This made it much easier to make choices about where to direct staff, and generally funnel resources so that they are used to the greatest possible effect.

Obviously, the ability to predict surges was enormously useful during the height of the pandemic. Predict surges huh? I didn’t realize that’s what they were doing. Ok, so it wasn’t perfect. Data isn’t a crystal ball. It’s there to recognize patterns and help people make reasonable conclusions based on the findings.

That’s what it did during the height of the pandemic — using nationwide numbers to make local predictions for when to expect the emergence of variants, or just general upcoming surges.

Now, the worst of the pandemic is behind us, but there are still many ways that data can be used to help make accurate forecasts on potential healthcare issues.

For example, going into the fall, analytic software might be able to forecast the severity of the flu season based on rates of infection, what variants are in circulation, and what percentage of the population is getting vaccinated.

Using this data, hospitals can better prepare for an influx of new patients. They are also better positioned for community outreach. Covid and flu vaccine outreach efforts tend to do better when a personal dimension can be incorporated into the outreach efforts.

A person may not be concerned about their risk, but if they understand how risky this year’s flu season is for their aged grandmother, they may feel more inclined to get vaccinated.

Of course, data isn’t all about helping people on the macro level. It’s also being used to give people highly personalized healthcare recommendations based on things like their personal/family health history, as well as global trends relating to healthcare outcomes for people that fall into the patient's demographic.

Instead of getting vague health-related suggestions — exercise more, eat right — the patient can get highly specific recommendations. Eat this. Exercise in this way. Try to keep your blood pressure and cholesterol within this range.

Data is also being used to improve outcomes within cancer research and treatment. For example, scientists are able to take broad data outcomes on patients who have survived for above-average periods of time with colon cancer. While they can’t necessarily determine why one person lives and another dies, they can identify commonalities among long-term survivors.

Maybe there is a common element in how their immune system attacks the cancer cells. Maybe they have X genetic factor, that can be distilled and distributed at a broader level.

Data has always been an important element of drug development, but now the technology is so much quicker than it used to be. Outcomes are improving, and scientists have entered exciting clinical phases on a wide range of promising cancer treatment types.

There have even been data-driven studies on lung cancer survival rates. Only around five to six percent of people with lung cancer recover through chemotherapy after a certain stage.

That means that an enormous number of people suffer needlessly through
an incredibly grueling round of therapy.

Using data, researchers are working to identify the common element in people who do recover from chemotherapy. This can those who aren’t likely to benefit from the treatment make a more informed decision about what to do next.

Naturally, privacy is a big concern whenever data is discussed. Never is this more true than in the healthcare sector, where information is very sensitive, and confidentiality laws are very strict. When it comes to big data, private information is generally anonymous and safe.

However, electric records and private healthcare data do carry the risk of public exposure. There was a notable instance of this in Ireland when Russian hackers held the national healthcare system hostage with ransomware. Hundreds of people had their personal information leaked.

That’s bad. Fortunately, it’s not a common experience. For one thing, data encryption is a legally mandated part of handling digital health information.

There are still risks. The hospitals are largely responsible for helping to neutralize them, but some of the responsibility lies with the patient as well. We’re far beyond the point of putting the data genie back in the bottle, but you can minimize your personal risk by practicing good data hygiene.

Keep your passwords private. Sign out of healthcare-related apps and websites after use, and make sure personal devices that carry private information are carefully secured.

Human error is the number one cause of data breaches. You can keep your records safe by remaining vigilant.